Firstly, he argued that computable data is about presenting the "data behind the data", for what he terms "citizen computation" - or "trying to get the answers, not other people's answers". Now this I agree with, as we note that Gresham's Law is increasingly applying to online data, ie bad data is driving out good. I also buy his argument that discussions about decisions is "where the sliders are on data models, not simple Black/White answers" that pass for public and media debate. The issue I have with this though is the level of maths capability required of everyone - and in the UK at any rate, maths and the whole STEM area is relatively lowly valued among the university educated (and relatively lowly paid in the UK), never mind teaching much higher levels of maths skills to les autres. Wolfram argues that Maths needs to be taught differently in schools, and that Computable Data now is like computers in the Assembler days, and we need need to get to "Mac" layer of computable data fast.

But that was all by the by, what really did interest me in the talk were three other points he made:

1. The Value Chain of Knowledge - here are some notes I made:

- Base data becomes less valuable, ability to analyse data grows in importance.
- Curation of good data will rise in value
- Bandwidth no longer = $ so everyone now pumps a lot out, so the pressure is on curation not creation (data, data everywhere)

Thus A Guiding Rule: Compositional knowledge >> dead information

2. What Data is most likely to emerge first? Mainly data that is either publically funding, or largely yours, eg:

Publicly funded R&D - should be computable
You! (Facebook etc) - whether you like it or not, it will be available
Governments - still lots of info locked up, and they are still biggest users of own data.

He points out it is necessary to unpack simple metric data, (exam results, school league tables) as they are both easy to game and not hugely informative. Work off computable data, not metrics
Corporate information - a lot is missing ( very private)

3. National Productivity - the Computable country needs a computable knowledge economy, which requires:

There was a rather fascinating angle on this, during the question phase. Essentially the discussion had moved to getting corporate data out from behind the firewall (he argues for a VRM-like ownership of your own data) but the point was made that privatisation is bad for Big Infrastructure and some other areas, so maybe a Computable Country should re-nationalise some areas as the data creates more value in a public entity than in a private one*.

Now THAT is an interesting argument, if one started to calculate the value of the chained data....the economics of Open Data may move from "Interesting" to "Critical"

(*Somehow I don't think the Policy Exchange would have intended this, being a right wing think tank )

Very interesting thought that computable data should be a public good. Note that the US government has made more data available than anyone else and the US is a leader in this area. Ironic given their general small government leanings.

Making government data publicly available and compiling quality stats only scratches the surface though. The big prize would be pulling private data into the public sphere. The challenge will be finding a model that makes it pay for the producer and doesn't stifle innovation.

The issue with private data is that much of it (in my experience anyway) outside the ERP/CRM systems is largely unstructured and even that in those systems is hard/expensive to get out never mind make "computable", and private companies will not necessarily see the value of what they have to offer others (or be paid for it) until much later in the day.

The implication of what Wolfram was saying is that some private data (especially around areas of major public spending/wish to save) is far more valuable in the public space as it allows the knock on value to be captured elsewhere

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